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Bi-objective decision support system for task-scheduling based on genetic algorithm in cloud computing

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Abstract

We address in this paper the task-scheduling in cloud computing. This problem is known to be \({\mathcal {NP}}\)-hard due to its combinatorial aspect. The main role of our model is to estimate the time needed to run a set of tasks in cloud and in turn reduces the processing cost. We propose a genetic approach for modelling and optimizing a task-scheduling problem in cloud computing. The experimental results demonstrate that our solution successfully competes with previous task-scheduling algorithms. For this, we develop a decision support system based on the core of CloudSim. In terms of processing cost, the obtained results show that our approach outperforms previous scheduling methods by a significant margin. In terms of makespan, the obtained schedules are also shorter than those of other algorithms.

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Authors and Affiliations

  1. LARODEC, Institut Supérieur de Gestion, Université de Tunis, Tunis, Tunisia

    Hatem Aziza & Saoussen Krichen

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  1. Hatem Aziza
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  2. Saoussen Krichen
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Correspondence to Hatem Aziza.

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Aziza, H., Krichen, S. Bi-objective decision support system for task-scheduling based on genetic algorithm in cloud computing. Computing 100, 65–91 (2018). https://doi.org/10.1007/s00607-017-0566-5

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  • DOI: https://doi.org/10.1007/s00607-017-0566-5

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